Method and system for unsupervised anomaly detection and accountability with majority voting for high-dimensional sensor data

An apparatus for detecting an anomaly in operation of a machine, comprising:

• one or more processors;
• a set of sensors embedded in the one or more machines;
• and
• memory storing instructions that, when executed by the one or more processors, cause the apparatus to:
• recording, via a set of sensors associated with the machine, to obtain sensor data associated with the machine while the machine is operating, wherein the set of sensors include a target sensor and a set of feature sensors;
• store the sensor data in the computing device;
• perform data exploration on the stored sensor data including repairing missing values and ranking the set of feature sensors, wherein the data exploration comprises one or more of:
• improving quality of the sensor data;
• identifying a subset of feature sensors from the set of feature sensors that are relevant for subsequent processing;
• and
• reducing amount of sensor data and hence computational cost involved in the subsequent processing;
• perform, based on the identified subset of feature sensors and the target sensor, unsupervised machine-learning to build a set of pair-wise univariate models;
• and
• determine whether and how an anomaly occurs in the operation of the machine based on the set of pair-wise univariate models.

A non-transitory computer-readable storage medium storing instructions that when executed by a computer cause the computer to perform a method for detecting an anomaly in operation of a machine, the method comprising:

• recording, via a set of sensors associated with the machine, to obtain sensor data associated with the machine, wherein the set of sensors include a target sensor and a set of feature sensors;
• storing the sensor data in the computing device;
• performing data exploration on the stored sensor data including repairing missing values and ranking the set of feature sensors, wherein the data exploration comprises one or more of:
• improving quality of the sensor data;
• identifying a subset of feature sensors from the set of feature sensors that are relevant for subsequent processing;
• and
• reducing amount of sensor data and hence computational cost involved in the subsequent processing;
• performing, based on the identified subset of feature sensors and the target sensor, unsupervised machine-learning to build a set of pair-wise univariate models;
• and
• determining whether and how an anomaly occurs in the operation of the machine based on the set of pair-wise univariate models.